Chapter 8. Quality Assurance

In this chapter, we will cover the following topics:

• Installing Pyflakes

• Performing static analysis with Pyflakes

• Analyzing code with Pylint

• Performing static analysis with Pychecker

• Testing code with docstrings

• Writing unit tests

• Testing code with mocks

• Testing the BDD way

Introduction

Quality assurance, contrary to popular belief, is not so much about finding bugs as it is about preventing them. We will discuss two ways to improve the code quality, thereby preventing issues. First, we will do static analysis of already existing code. Second, we will cover unit testing; this includes mocking and Behavior Driven Development (BDD).

Installing Pyflakes

Pyflakes is a Python code analysis package. It can analyze your code, and spot potential problems such as:

• Unused imports

• Unused variables

Getting ready

Install pip or easy_install, if necessary.

How to do it...

Choose one of the following listed options to install pyflakes:

• Installing with pip.

  We can install pyflakes with the pip command:

  sudo pip install pyflakes
  

• Installing with easy_install.

  We can install pyflakes with the easy_install command:

  sudo easy_install pyflakes
  

• Installing on Linux.

  The Linux package name is pyflakes as well. For instance, on Red Hat do the following:

  sudo yum install pyflakes

  On Debian/Ubuntu, the command is:

  sudo apt-get install pyflakes
  

Performing static analysis with Pyflakes

We will perform static analysis of a part of the NumPy codebase. In order to do this, we will check out the code using Git. We will then run static analysis on part of the code using pyflakes.

How to do it...

1. Check out the code.

   To check out the NumPy code, we will need Git. Installing Git is outside the scope of this book. The Git command to retrieve the code is as follows:

   git clone git://github.com/numpy/numpy.git numpy
   

   Alternatively, we can download a zip archive from https://github.com/numpy/numpy .

2. Analyze the code.

   The previous step should have created a numpy directory with all the NumPy code. Go to this directory, and within it run the following command:

   $ pyflakes *.py
   pavement.py:71: redefinition of unused 'md5' from line 69
   pavement.py:88: redefinition of unused 'GIT_REVISION' from line 86
   pavement.py:314: 'virtualenv' imported but unused
   pavement.py:315: local variable 'e' is assigned to but never used
   pavement.py:380: local variable 'sdir...

Analyzing code with Pylint

Pylint is another open source static analyzer originally created by Logilab. Pylint is more complex than Pyflakes; it allows more customization. However, it is slower than Pyflakes. For more information check out http://www.logilab.org/card/pylint_manual.

In this recipe, we will again download the NumPy code from the Git repository—this step is omitted for brevity.

Getting ready

You can install Pylint from the source distribution. However, there are many dependencies, so you are better off installing with either easy_install, or pip. The installation commands are as follows:

easy_install pylint
sudo pip install pylint

How to do it...

We will again analyze from the top directory of the NumPy codebase. Please notice that we are getting much more output. In fact, Pylint prints so much text that most of it had to be cut out here:

pylint *.py
$ pylint *.py
No config file found, using default configuration
************* Module pavement
C: 60: Line too long (81/80)
C:139...